臺灣博碩士論文加值系統

背景：國外健康保險申報資料庫的研究，正確性多介於64%至82%之間。而台灣目前還沒有針對特定單一疾病做申報資料的正確性分析，所以本研究目的是要評估台灣全民健保申報糖尿病資料的正確性，以及影響全民健保申報糖尿病資料正確性的因素，並建構預測全民健保糖尿病資料正確性的預測模式，與估算台灣地區糖尿病的盛行率。
研究方法：有關利用率分析，取自全民健保2000年全國醫院、診所申報所有糖尿病病人資料分析。在正確性分析方面，從全民健保所有糖尿病病人資料，以分層、兩階段、PPS與等機率方法抽樣9,000人寄發問卷。以病人自述病情問卷為黃金標準，分析1,350份問卷，並串聯藥物醫令檔以求得全民健保申報資料的正確性，再利用邏輯式迴歸（Logistic Regression）分析影響因素，且利用E-M Algorithm方法求得預測全民健保糖尿病資料正確性的預測模式。
結果：利用率分析方面，糖尿病病人每年因糖尿病原因（全部原因）的門診、急診與住院次數為8.51（30.04）、0.10（0.50）與0.30（0.48）次，整年度花費共242.77（531.38）億元，住院費用即佔55.07%（41.23%）。糖尿病病人之就醫地點主要為醫院，佔78.64%，就醫科別則以內科為主，佔70.72%。正確性分析方面，1,350份問卷中確定有糖尿病者1,007人，佔74.59%。經單變項分析對正確性有影響的因素為醫院評鑑等級、病人年齡、性別、就醫科別、併發症種類、門診、急診與住院次數。多變項邏輯式迴歸分析發現，影響正確性的因素有病人門診次數、住院次數、年齡與醫院評鑑等級。利用E-M Algorithm方法求得預測全民健保糖尿病資料正確性的預測模式為Logit P= -23.08 + (-3.75*醫院評鑑等級) + (5.78*年齡) + (14.30*病人住院次數) + (17.29*病人門診次數)。由預測模式估算，2000年全民健保糖尿病資料的正確性為70.42%，台灣地區糖尿病全人口盛行率為2.844%，四十歲以上盛行率為7.576%。若考慮加上未被診斷的糖尿病患者，台灣目前可能有103萬個糖尿病病人。
結論：2000年全民健保申報糖尿病資料的正確性是70.42%，若只取門診三次以上才認定為糖尿病個案，則正確性就提高為88.81%。另以預測模式估算，其正確性為89.19%，若再加入藥物醫令檔資料，其正確性就高達為91.41%。未來，將可以此預測模式作為利用率分析之調整。

Background： Overall accuracy of insurance claims data from the National Health Insurance database is around 64%~82% in other countries. In Taiwan, there has been no study about accuracy of insurance claims data for specific disease yet. Therefore, the objectives of this study are to evaluate accuracy of diabetes insurance claims data in Taiwan, to analyze factors influencing accuracy, to build up a predicting model for diabetes insurance claims data, and to estimate the diabetes prevalence rate in Taiwan based on this model.
Methods： We use diabetes claims data reported from hospitals and clinics in 2000 on utilization analysis. For accuracy analysis, 9,000 diabetes patients are sampled from the National Health Insurance Research Database by using stratified, two-staged, PPS (probability proportional to size), and equal probability sampling method. The golden standard is based on patients’ self-reports from mailing questionnaires. Besides, data is modified by linkage with pharmacy claims data. We apply the logistic regression analysis to examine the factors relevant to accuracy of insurance claims data. We also use the method of E-M Algorithm to derive the predicting model for accuracy of diabetes insurance claims data.
Results： We find that average times of outpatient visits, emergent visits, and hospitalization due to diabetes (all causes) are 8.51 (30.34), 0.10 (0.50), and 0.30 (0.48), separately. The total expenditure on diabetes is NTD 24.277 (53.138) billion, 55.07% (41.23%) for hospitalization. Diabetes is mainly followed up in the hospital (78.64%) and medical department accounts for 70.72%. We collect 1,350 questionnaires and define 1,007 (74.59%) as diabetes cases. Univariate analysis indicates that the rank of assessment on hospitals, age, sex, follow-up department, types of complication, times of outpatient and emergent visits as well as hospitalization are factors influencing accuracy. Multivariate logistic regression analysis shows that times of outpatient visits and hospitalization, age, and the rank of assessment on hospitals are statistically significant. By the method of E-M Algorithm, we conclude that the equation in our predicting model is: logit p= -23.08 + (-3.75* the rank of assessment on hospitals) + (5.78*age) + (14.30* times of hospitalization) + (17.29* times of outpatient visits). Based on the model, we estimate that accuracy of diabetes insurance claims data in Taiwan is 70.42%. The overall prevalence rate of diagnosed diabetes is 2.84% and that of the age over 40 is 7.576%. There will be approximately 1.03 million diabetes patients in Taiwan when undiagnosed cases incorporated.
Conclusions： In Taiwan, accuracy of diabetes insurance claims data is 70.42%. If patients with more than three times of outpatient visits are selected as diabetes cases, accuracy will increase to 88.81%. Accuracy estimated by our predicting model is 89.19%. The result will be 91.41% if data rectified by pharmacy claims data. We hope that application of the model in our study can be helpful to justify utilization analysis.

摘要 1
第壹章 緒論 4
第一節 研究背景 4
第二節 研究目的 7
第貳章 文獻探討 8
第一節 國外研究健康保險申報資料庫正確性的情況 10
第二節國外研究健康保險申報資料庫單一種疾病正確性的情況 13
一、有關糖尿病 13
二、其他疾病 14
第三節 國內研究健康保險申報資料庫正確性的情況 16
第四節 以病人自述病情的方法研究正確性資料有關文獻 18
第參章 研究材料與方法 21
第一節 研究流程與架構 21
一、研究流程 21
二、研究架構 22
第二節 研究材料與方法 23
一、目標族群的建立 23
二、研究族群的建立 25
三、樣本族群的建立 27
四、預後的測量：糖尿病個案的確定。 28
五、分析評估影響全民健保申報資料正確性的因素。 28
六、建構全民健保糖尿病資料正確性的預測模式 31
七、利用預測模式估算台灣糖尿病的盛行率。 31
第三節 研究變項與操作型定義 33
一、依變項： 33
二、自變項： 33
第四節資料處理與分析 35
第肆章 結果 36
第一部分 2000年全民健保糖尿病資料的利用率分析 36
第一節 整體分析 36
一、盛行率 36
二、門、急診與住院利用率 37
三、費用利用情況 38
第二節 依年齡別分析 42
一、盛行率 42
二、門診利用率 42
三、急診利用率 43
四、住院利用率 43
五、費用利用情況 44
第三節 依醫院評鑑等級分析 50
一、利用率 50
二、門、急診與住院利用率 50
三、費用利用情況 51
第四節 依就醫科別分析 55
一、利用率 55
二、門、急診與住院利用率 55
三、費用利用情況 56
第二部分 全民健保糖尿病資料的正確性分析 58
第一節 研究族群與目標族體，樣本族群與研究族群之比較 58
一、研究族群與目標族群之比較 58
二、樣本族群與研究族群之比較 59
第二節 樣本族群的初步結果 65
第三節 評估影響全民健保糖尿病資料正確性的因素 66
一、醫療提供方面： 66
二、病人基本資料： 67
三、影響正確性因素之多變項邏輯式回歸分析： 69
第四節、建構全民健保糖尿病資料正確性之預測模式 74
一、預測模式之建構 74
二、正確性影響因素之相關分析 76
三、預測模式之分析流程圖 80
四、預測模式之正確性 82
第五節 利用預測模式估算台灣已被診斷糖尿病的盛行率 84
一、2000年台灣全民健保糖尿病資料的正確性 84
二、估算台灣已被診斷糖尿病的盛行率 84
第伍章 討論與結論 86
第一節 討論 86
第一部分、全民健保糖尿病資料利用率分析 86
第二部分、全民健保糖尿病資料正確性分析 88
第二節 研究限制 92
第三節結論與建議 94
一、結論 94
二、建議 96
參考文獻 97
附錄 106

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